TY - GEN
T1 - Predictors of Sales and the Covid-19 Disruption
T2 - 26th Pacific Asia Conference on Information Systems, PACIS 2022
AU - Sindihebura, Tanguy Tresor
AU - Pu, Xiaodie
AU - Chen, Jin
N1 - Publisher Copyright:
© 2022, Association for Information Systems. All rights reserved.
PY - 2022
Y1 - 2022
N2 - E-commerce platforms have heavily relied on predictive machine learning models to leverage the massive data generated in daily operations. However, by altering online customer purchasing behavior, Covid-19 has distorted sales prediction models used by sellers and e-commerce platforms, which may lead them to inaccurate strategic decisions. Using a dataset comprised of electronics products from Amazon, the preliminary results shows that the importance of several predictors of online sales has changed after the beginning of the pandemic. In particular, the importance of negative related factors was found to have significantly increased after the start of Covid-19. Furthermore, adding aspect-based sentiments was found to significantly improve sales forecasting especially during the period after the beginning of Covid19. The study contributes to the literature evaluating the effects of Covid-19 on e-commerce by providing an in-depth understanding of these effects from an unexplored perspective of prediction models.
AB - E-commerce platforms have heavily relied on predictive machine learning models to leverage the massive data generated in daily operations. However, by altering online customer purchasing behavior, Covid-19 has distorted sales prediction models used by sellers and e-commerce platforms, which may lead them to inaccurate strategic decisions. Using a dataset comprised of electronics products from Amazon, the preliminary results shows that the importance of several predictors of online sales has changed after the beginning of the pandemic. In particular, the importance of negative related factors was found to have significantly increased after the start of Covid-19. Furthermore, adding aspect-based sentiments was found to significantly improve sales forecasting especially during the period after the beginning of Covid19. The study contributes to the literature evaluating the effects of Covid-19 on e-commerce by providing an in-depth understanding of these effects from an unexplored perspective of prediction models.
KW - aspect-based sentiments
KW - Covid-19
KW - sales forecasting
KW - sentiment analysis
UR - https://www.scopus.com/pages/publications/105029285879
M3 - Conference contribution
AN - SCOPUS:105029285879
SN - 9781958200018
T3 - Pacific Asia Conference on Information Systems
BT - Pacific Asia Conference on Information Systems, PACIS 2022
PB - Association for Information Systems
Y2 - 5 July 2022 through 9 July 2022
ER -